research-article

View Resistant Gait Recognition

Authors:

Anton KonushinAuthors Info & Claims

ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

Pages 7 - 12

https://doi.org/10.1145/3376067.3376083

Published: 25 February 2020 Publication History

Abstract

Human gait is one of the biometric characteristics that a person can be identified by. However, the wide applicability of gait recognition in real life is prevented by a great variety of conditions that affect the gait representation, such as different viewpoints. In this work, we present a novel view resistant approach to overcome the multi-view recognition challenge. The new loss function is proposed to increase the stability of the model to view changes. Besides this, the cross-view embedding of the gait features is made to enhance their discriminant ability which improves the recognition accuracy as well. The proposed approaches show a significant gain in quality and allow to achieve the state-of-the-art accuracy on the most common benchmark and outperform the most successful model on the majority of the views and on average.

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Cited By

Wang PJiang RLiu CLuo J(2024)AGR: Acoustic Gait Recognition Using Interpretable Micro-Range ProfileIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621283(1201-1210)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621283
Khaliluzzaman MUddin ADeb KHasan M(2023)Person Recognition Based on Deep Gait: A SurveySensors10.3390/s2310487523:10(4875)Online publication date: 18-May-2023
https://doi.org/10.3390/s23104875
Sepas-Moghaddam AEtemad A(2023)Deep Gait Recognition: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.315186545:1(264-284)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPAMI.2022.3151865
Show More Cited By

Index Terms

View Resistant Gait Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
      2. Computer vision tasks
        Biometrics

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cover image ACM Other conferences

ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing

December 2019

270 pages

ISBN:9781450376822

DOI:10.1145/3376067

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shanghai Jiao Tong University: Shanghai Jiao Tong University
Xidian University
TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2020

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ICVIP 2019

ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing

December 20 - 23, 2019

Shanghai, China

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Cited By

Wang PJiang RLiu CLuo J(2024)AGR: Acoustic Gait Recognition Using Interpretable Micro-Range ProfileIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621283(1201-1210)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621283
Khaliluzzaman MUddin ADeb KHasan M(2023)Person Recognition Based on Deep Gait: A SurveySensors10.3390/s2310487523:10(4875)Online publication date: 18-May-2023
https://doi.org/10.3390/s23104875
Sepas-Moghaddam AEtemad A(2023)Deep Gait Recognition: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.315186545:1(264-284)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPAMI.2022.3151865
Ruiz-Barroso PCastro FDelgado-Escaño RRamos-Cózar JGuil N(2022)High performance inference of gait recognition models on embedded systemsSustainable Computing: Informatics and Systems10.1016/j.suscom.2022.10081436(100814)Online publication date: Dec-2022
https://doi.org/10.1016/j.suscom.2022.100814
Marin-Jimenez MCastro FDelgado-Escano RKalogeiton VGuil N(2021)UGaitNet: Multimodal Gait Recognition With Missing Input ModalitiesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.313257916(5452-5462)Online publication date: 2021
https://doi.org/10.1109/TIFS.2021.3132579

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